Quantum Machine Learning Optimization Based on Quantum Noise

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the potential of leveraging quantum noise as an additional optimization signal within quantum machine learning (QML) algorithms. The core claim is that controlled quantum noise, rather than being detrimental, can significantly enhance the training efficiency of QML models. The proposed mechanism involves introducing specifically designed quantum noise into the training process and utilizing the information inherent within this noise to guide gradient estimation and parameter updates. This approach represents a novel strategy for QML optimization, moving beyond purely deterministic methods. We explore theoretical frameworks and mathematical formulations to demonstrate the viability and potential benefits of this approach. The key contribution lies in recognizing and harnessing the information encoded within quantum noise, offering a new pathway for accelerating the training of complex QML models. This research provides a foundation for future investigations into noise-aware QML algorithms.

DOI: 10.5281/zenodo.22155995

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